Shift and convolution in matrix form

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SUMMARY

The discussion focuses on the mathematical representation of shift and convolution operators in matrix form, specifically using the backward shift operator and convolution as Z-transforms. The backward shift operator, denoted as z, acts on a vector to translate its elements, while convolution is expressed as y = H u, where H is a matrix derived from the impulse response h. The participants explore the derivation of matrix H from the operator composition and its implications for shifting and scaling vectors.

PREREQUISITES
  • Understanding of linear algebra, specifically matrix operations.
  • Familiarity with Z-transforms and their applications in signal processing.
  • Knowledge of convolution operations in discrete-time systems.
  • Experience with vector representation of mathematical operations.
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  • Study the derivation of Z-transforms in detail, focusing on their application in signal processing.
  • Learn about the properties of convolution and its relationship with linear systems.
  • Explore matrix representations of linear operators, particularly in the context of discrete-time signals.
  • Investigate the implications of shifting operators in matrix form and their applications in various engineering fields.
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Mathematicians, signal processing engineers, and students studying linear systems who seek to deepen their understanding of shift and convolution operations in matrix form.

valjok
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The operators act on a vector to produce another vector. They are matrices, therefore. For instance, the backward shift (aka delay) operator, z, acting on vector, say y, translates k-th element into k-1-th: zyk = yk-1. It is normally z-1 in z-transform but I will ignore the difference where it is clear. I guess that the long matrix form of operator is

[tex] z\left[\begin{array}{c}y_0\\y_1\\y_2\\y_3\end{array}\right]<br /> = \left[\begin{array}{cccc}z&0&0&0\\0&z&0&0\\0&0&z&0\\0&0&0&z\end{array}\right]<br /> \left[\begin{array}{c}y_0\\y_1\\y_2\\y_3\end{array}\right]<br /> = \left[\begin{array}{c}zy_0\\zy_1\\zy_2\\zy_3\end{array}\right]<br /> = \left[\begin{array}{c}0\\y_0\\y_1\\y_2\end{array}\right] [/tex]

Correct me if I am wrong identifying the diagonal matrix with multiplication operator. Similarly to scalar operator, say 3, that uniformly multiplies every element of vector by the same factor 3, shift operator manages to replace it by the neighbor.


Now, the convolution

[tex] y[m] = \sum_{k=0}{h[m-k]u[k]} = \sum_{k=0}{h[k]u[m-k]} = \sum_{k=0}{h[k]z^{-k}u[m]} = H(z) u[m][/tex]

happens to be a Z-transform. In vector form, convolution turns out to

y = H u

and expanded into matrices

[tex] H\left[\begin{array}{c}u_0\\u_1\\u_2\\u_3\end{array}\right]<br /> = \left[\begin{array}{cccc}h_0&0&0&0\\h_1&h_0&0&0\\h_2&h_1&h_0&0\\h_3&h_2&h_1&h_0\end{array}\right]<br /> \left[\begin{array}{c}u_0\\u_1\\u_2\\u_3\end{array}\right][/tex]

The thing that I do not understand is that how matrix H is derived from the basic operator composition [tex]\sum_{k=0}{h[k]z^{-k}}[/tex], from their matrices.

That is, I see that it can be decomposed

[tex] \left[\begin{array}{cccc}h_0&0&0&0\\h_1&h_0&0&0\\h_2&h_1&h_0&0\\h_3&h_2&h_1&h_0\end{array}\right] <br /> = [\begin{array}{cccc}h&h&h&h\end{array}]\left[\begin{array}{cccc}z^0&0&0&0\\0&z^1&0&0\\0&0&z^2&0\\0&0&0&z^3\end{array}\right] = [h] [\begin{array}{cccc}z^0&z^1&z^2&z^3\end{array} ][/tex]

Nevertheless, the problem is that H must shift u while it looks like h that is shifted in the matrix. If I start reducing H u by shifting u first, before scaling by h:

y = H u = (h [z0 z1 z2 z3 ]) u = h ([z0 z1 z2 z3 ] u) - matrix multiplication is associative

= h [z0u0+z1u1+z2u2+z3u3 ]
= 4u0 h,

which is bizarre.
 
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In the end it is a linear equation system which must be solved. No big deal.
However, "they" are matrices leaves open whether the matrices are to be shifted, or whether the operators are matrices. Write down a few examples for ##n=2,3,4## and you will probably see a pattern.
 

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